Our objective is to simultaneously assess the extent to which neighborhood of residence and embeddedness in defined egocentric social networks affect health. Since both networks and neighborhoods have effects on health, and since the presence of social network contacts might confound the effect of neighborhoods, our overarching objective is to ascertain the relative importance of neighborhood effects and the propinquity of social network ties. We have four specific aims. First, we will embellish a longitudinal dataset, based on the Framingham Heart Study (FHS), describing 5,124 individuals ("egos") and a social network of 12,630 people in which they are embedded (their possible "alters"), by adding detailed geocoding data for all these people measured roughly every four years from 1971 to the present. As part of this aim, we will prepare detailed maps of where people and their social network contacts reside and visualize various neighborhood-level measures such as local wealth or crime. Second, we will describe the overlap between egos'social network and neighborhood ties. We will examine the geographic distance between our egos and various kinds of alters and assess factors associated with this distance (e.g., the proximity of sisters versus brothers). We will also assess which individuals have parochial (i.e., same neighborhood or same town) and which ones have cosmopolitan (i.e., outside) ties. Are there attributes of individuals that seem to foster maintenance of geographic propinquity? We will also examine, over more than 32 years of follow up, the determinants of individuals'choice of neighborhoods in which to reside. Third, using a multi-level modeling framework, we will explore whether there are neighborhood effects on health outcomes in the FHS (the first time that the FHS has been used for this purpose). We will examine the dependence of individual health outcomes (i.e., mortality, cardiovascular disease, hypertension, obesity, depression, disability, and self-assessed health) on both individual attributes and on the attributes of neighborhoods (measured at the Census tract or block group level). We hypothesize that, in addition to substantial and significant variation at the individual level, there will be significant supra-individual variation in each of the outcomes that is attributable to supraindividual neighborhood context. Fourth, using a multi-level modeling framework, we will evaluate the potential contribution of residing near one's social network contacts in explaining neighborhood effects on individual health outcomes. This work is policy-relevant since it will help localize the level at which such supra-individual health effects can arise and since it address the outcomes such as cardiovascular disease, depression, and obesity, all of which are leading causes of morbidity in the elderly.